Improving Human Performance in a Real Operating Environment through Real-Time Mental Workload Detection

The ability to directly detect mental over- and under-load in human operators is an essential feature of complex monitoring and control processes. Such processes can be found, for example, in industrial production lines, in aviation, as well as in common everyday tasks such as driving. In this chapter, we present an EEG-based system that is able to detect high mental workload in drivers operating under real traffic conditions. This information is used immediately to mitigate the workload typically induced by the influx of information that is generated by the car's electronic systems. Two experimental paradigms were tested: an auditory workload scheme and a mental calculation task. The result is twofold. The system's performance is strongly subject-dependent; however, the results are good to excellent for the majority of subjects.We show that in these cases an induced mitigation of a reaction time experiment leads to an increase of the driver's overall task performance.